#!/bin/bash
set -x
#当前路径,不需要修改
cur_path=`pwd`
export PYTHONPATH=${cur_path}/../pylib/src:$PYTHONPATH
#集合通信参数,不需要修改

export RANK_SIZE=1
export JOB_ID=10087
RANK_ID_START=0
RankSize=1

#使能RT2.0
export ENABLE_RUNTIME_V2=1

# 数据集路径,保持为空,不需要修改
data_path=""
#export ASCEND_SLOG_PRINT_TO_STDOUT=1

#基础参数，需要模型审视修改
#网络名称，同目录名称
Network="PixelLink_ID3056_for_TensorFlow"
#训练epoch
train_epochs=
#训练batch_size
batch_size=24
#训练step
train_steps=200
#学习率
learning_rate=

#维测参数，precision_mode需要模型审视修改
precision_mode="allow_fp32_to_fp16"
#维持参数，以下不需要修改
over_dump=False
data_dump_flag=False
data_dump_step="10"
profiling=False

# 帮助信息，不需要修改
if [[ $1 == --help || $1 == -h ]];then
    echo"usage:./train_performance_1P.sh <args>"
    echo " "
    echo "parameter explain:
    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
    --over_dump		           if or not over detection, default is False
    --data_dump_flag		     data dump flag, default is False
    --data_dump_step		     data dump step, default is 10
    --profiling		           if or not profiling for performance debug, default is False
	--data_path		           source data of training
    -h/--help		             show help message
    "
    exit 1
fi

#参数校验，不需要修改
for para in $*
do
    if [[ $para == --precision_mode* ]];then
        precision_mode=`echo ${para#*=}`
    elif [[ $para == --over_dump* ]];then
        over_dump=`echo ${para#*=}`
        over_dump_path=${cur_path}/output/overflow_dump
        mkdir -p ${over_dump_path}
    elif [[ $para == --data_dump_flag* ]];then
        data_dump_flag=`echo ${para#*=}`
        data_dump_path=${cur_path}/output/data_dump
        mkdir -p ${data_dump_path}
    elif [[ $para == --data_dump_step* ]];then
        data_dump_step=`echo ${para#*=}`
    elif [[ $para == --profiling* ]];then
        profiling=`echo ${para#*=}`
        profiling_dump_path=${cur_path}/output/profiling
        mkdir -p ${profiling_dump_path}
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    fi
done

#校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi

#训练开始时间，不需要修改
start_time=$(date +%s)

#进入训练脚本目录，需要模型审视修改
cd $cur_path/..

for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
do
    #设置环境变量，不需要修改
    echo "Device ID: $ASCEND_DEVICE_ID"
    export RANK_ID=$RANK_ID

    #创建DeviceID输出目录，不需要修改
    if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
        rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
    else
        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
    fi
    
    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
    nohup python3 train_pixel_link.py \
            --train_dir=./models/pixel_link \
            --num_gpus=1 \
            --learning_rate=1e-3 \
            --gpu_memory_fraction=-1 \
            --train_image_width=512 \
            --train_image_height=512 \
            --batch_size=${batch_size}\
            --dataset_dir=${data_path} \
            --dataset_name=icdar2015 \
            --dataset_split_name=train \
            --max_number_of_steps=${train_steps} \
            --checkpoint_path=${CKPT_PATH} \
            --using_moving_average=1 > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
done 
wait

#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"

#单迭代训练时长
TrainingTime=`grep 'loss =' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |awk -F "(" '{print $2}' |awk -F " " '{print $1}' |tail -10|awk '{sum+=$1}END {print"",sum/NR}'|sed s/[[:space:]]//g`
# #输出性能FPS，需要模型审视修改
FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${TrainingTime}'}'`
#打印，不需要修改
echo "Final Performance item/sec : $FPS"

#输出CompileTime
CompileTime=`grep "sec/step" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |head -n 1|awk '{print $7}' | awk -F '(' '{print $2}'`

# #输出训练精度,需要模型审视修改
#train_accuracy=`grep "test AUC" ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $3}'`
# #打印，不需要修改
#echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'RT2'_'perf'

#吞吐量
ActualFPS=${FPS}

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep 'loss =' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F " " '{print $6}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
#echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CompileTime = ${CompileTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
